METHOD FOR OPERATING A WIND POWER INSTALLATION, WIND POWER INSTALLATION AND WIND FARM

Abstract

The present disclosure relates to a method for operating a wind power installation, in particular for identifying unusual oscillation events, and an associated wind power installation and a wind farm. The method comprises the steps of: providing a parametrized limit for a value of an observed oscillation of a component of the wind power installation; determining a current limit from the parametrized limit taking account of at least one current ambient parameter, in particular an ambient parameter that is indicative for the current incident flow; determining a current value of the observed oscillation of the component; comparing the current value of the observed oscillation of the component with the current limit; and operating the wind power installation on the basis of the result of the comparison.

Claims

1. A method for operating a wind power installation, the method comprising: providing a parametrized limit for a value of an observed oscillation of a component of the wind power installation, the component being at least one of a tower head, a rotor blade, a generator, and a rotor, determining a current limit from the parametrized limit taking into account of at least one current ambient parameter, determining a current value of the observed oscillation of the component, comparing the current value of the observed oscillation of the component with the current limit, and operating the wind power installation based on the comparison.

2. The method according to claim 1, wherein the current value of the observed oscillation is a value that characterizes the observed oscillation or a value derived from the observed oscillation, including at least one of an acceleration, a maximum oscillation deflection, and a frequency component.

3. The method according to claim 1, wherein the ambient parameter comprises a wind speed, the wind speed being measured or determined by a wind estimator from a current operating point of the wind power installation.

4. The method according to claim 3, wherein the wind speed has a reference time period that is one of less than 1 minute, between 10 and 30 seconds, or less than 10 seconds.

5. The method according to claim 1, further comprising measuring the value of the observed oscillation on the component.

6. The method according to claim 5, wherein the measuring is carried out using at least one of an acceleration sensor, a gyroscope, an incremental encoder, a strain measurement, an optical sensor and a power measuring device.

7. The method according to claim 1, wherein the value of the observed oscillation is derived from measurements on a component of the wind power installation that differs from the observed component.

8. The method according to claim 7, wherein the observed component is the rotor of the wind power installation and a current value of the oscillation of the rotational speed of the rotor is derived from a generator current.

9. The method according to claim 7, wherein the component of the wind power installation is one or more of the following components: the tower head, with a tower head acceleration being observed, the rotor blade, with oscillations of a pitch angle and/or swivel load of the rotor blade being observed, the generator, with oscillations of a generated power of the generator being observed, and the rotor, with oscillations of a rotational speed of the rotor being observed.

10. The method according to claim 9, further comprising observing a tower head acceleration of the tower head, wherein comparing the tower head acceleration with the current limit comprises: filtering using a low-pass filtering of the tower head acceleration to observe components of the tower head acceleration of no more than 0.8 Hz, and/or determining an extremal point of the tower head acceleration, and comparing the extremal point of the tower head acceleration with the limit, and/or determining a mean amplitude of the tower head acceleration, and comparing the mean amplitude with the limit.

11. The method according to claim 9, further comprising observing oscillations of a pitch angle of the rotor blade, wherein comparing the current oscillation of the pitch angle with the current limit comprises: filtering the current oscillation of the pitch angle to eliminate wind-excited oscillations, and/or making a distinction between singular events, singular overshoots of the current limit, or prolonged events.

12. The method according to claim 9, further comprising observing oscillations of a swivel load of the rotor blade, and wherein comparing the current oscillation of the swivel load with the current limit comprises a compensation of an oscillation contribution whose frequency corresponds to the rotor rotation and corresponds in terms of absolute value to the gravitational contribution.

13. The method according to claim 9, further comprising observing oscillations of a power generated by the generator, and wherein comparing the current oscillations in the generated power with the current limit comprises: filtering the generated power to eliminate wind-excited oscillations, and/or compensating power changes induced by controlling the wind power installation.

14. The method according to claim 9, further comprising observing oscillations of a rotational speed of the rotor, and wherein comparing the current rotational speed of the rotor with the current limit comprises filtering the observed rotational speed to eliminate wind-excited oscillations.

15. The method according to claim 1, further comprising: determining an expected operating load of the wind power installation during normal operation, comparing the expected operating load of the wind power installation with a measured actual load of the wind power installation and adjusting the parametrized limit based on the comparison.

16. The method according to claim 15, wherein the adjustment of the parametrized limit comprises: increasing the parametrized limit by a predetermined increase factor when the parametrized limit has been exceeded, and/or reducing the parametrized limit by a predetermined reduction factor when the parametrized limit has not been exceeded over a predetermined period of time.

17. The method according to claim 1, wherein the operating the wind power installation based on the comparison comprises: detecting an unusual oscillation event when the current value of the observed oscillation overshoots or undershoots the current limit, and communicating the detected unusual oscillation event by way of SCADA.

18. The method according to claim 1, wherein the at least one current ambient parameter is an ambient parameter that is indicative for the current incident flow.

19. A wind power installation comprising a controller, wherein the controller is configured to implement the method according to claim 1.

20. A wind farm comprising a plurality of wind power installations according to claim 19.

Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0089] Further advantages and preferred configurations are described below with reference to the appended figures. In the figures:

[0090] FIG. 1 schematically shows a wind power installation by way of example;

[0091] FIG. 2 schematically shows a wind farm by way of example;

[0092] FIG. 3 schematically shows correlations of oscillations and ambient parameters by way of example;

[0093] FIG. 4 schematically shows correlations of oscillations and ambient parameters by way of example;

[0094] FIGS. 5a and 5b schematically show different oscillation events by way of example;

[0095] FIG. 6 schematically shows the detection of unusual oscillation events by way of example; and

[0096] FIGS. 7a and 7b schematically show the influence of an observation time period on the correlation by way of example.

DETAILED DESCRIPTION

[0097] FIG. 1 shows a schematic illustration of a wind power installation according to the disclosure. The wind power installation 100 has a tower 102 and a nacelle 104 on the tower 102. An aerodynamic rotor 106 having three rotor blades 108 and having a spinner 110 is provided on the nacelle 104. During the operation of the wind power installation, the aerodynamic rotor 106 is set in rotational motion by the wind and thereby also rotates an electrodynamic rotor or armature of a generator, which is coupled directly or indirectly to the aerodynamic rotor 106. The electric generator is arranged in the nacelle 104 and generates electrical energy. The pitch angles of the rotor blades 108 may be varied by pitch motors at the rotor blade roots 109 of the respective rotor blades 108.

[0098] FIG. 2 shows a wind farm 112 having, by way of example, three wind power installations 100, which may be identical or different. The three wind power installations 100 are thus representative of basically any desired number of wind power installations of a wind farm 112. The wind power installations 100 provide their power, specifically in particular the generated current, via an electrical farm grid 114. In this case, the respectively generated currents or powers of the individual wind power installations 100 are added together and a transformer 116, which steps up the voltage in the farm, is usually provided in order to then feed into the supply grid 120 at the infeed point 118, which is also generally referred to as a PCC (point of common coupling). FIG. 2 is only a simplified illustration of a wind farm 112. By way of example, the farm grid 114 may also be designed in another way by virtue of for example a transformer also being present at the output of each wind power installation 100, to mention just one other exemplary embodiment.

[0099] FIG. 3 schematically shows a correlation between observed oscillations of a component of the wind power plant 100 on the vertical axis and an ambient parameter on the horizontal axis by way of example. The observed oscillation in the graphs 310, 320, 330 plotted on the left is an acceleration of an upper region of the tower, also referred to as tower head, along the wind direction. In the graphs 340, 350, 360 plotted on the right, the observed oscillation is an acceleration of the mid region of the tower. The ambient parameter comprises a wind speed in graphs 310 and 340, a pitch angle in graphs 320 and 350, and a generator torque in graphs 330 and 360.

[0100] The existence of a particularly good correlation, that is to say a mapping between observed oscillation and ambient parameter that is as unique as possible, can be identified in graph 310 for the tower head acceleration and the wind speed. This likewise applies to the acceleration in the mid tower region in the graph 340. The pitch angles in graphs 320 and 350 are not unique, especially for a pitch angle of 0°. The correlation is unique for higher pitch angles. For the generator torque in graphs 330 and 360, the mapping from a moment to an acceleration is non-unique.

[0101] FIG. 4 shows six further graphs 410, 420, 430, 440, 450, 460, which exhibit a correlation between tower head acceleration (410, 420, 430) or acceleration in the middle of the tower (440, 450, 460) and further parameters. These values include a rotational speed in graphs 410, 440, the generated electrical power in graphs 420, 450 and a force on the rotor hub in graphs 430 and 460. None of the relationships shown in FIG. 4 exhibit a sufficient correlation for the present disclosure.

[0102] FIG. 5a schematically shows the curve of a first type of oscillation by way of example. A graph 510 shows the temporal progress of an acceleration. A dashed line is used to show a first limit 520, which for example is 25% above the average value of the oscillation. In this example, the assumption is made that the value of the oscillation 502 typically ranges between +1 and −1 m/s.sup.2.

[0103] Naturally, other acceleration values and limits are also conceivable. An extremal value threshold, for example 1.6, is provided as a second threshold 530. It is evident that the oscillation 502 exceeds the second threshold 530 at one point 540. Accordingly, this is referred to as an extreme event. This means that a very high acceleration is exceeded for a brief period of time.

[0104] By contrast, FIG. 5b shows a second case of an unusual oscillation, specifically an acceleration that is above the first threshold 520 for a relatively long period of time, specifically eight periods in this example. Preferably, the limit 520 being exceeded once does not lead to the detection of such an oscillation event but the threshold 520 has to be exceeded multiple times. To this end, it is possible for example to count the number of extremal values 550 that are successively located above the threshold 520, and compare this number to a predetermined value, for example 5 or 8. Naturally, a different number of overshoots is also possible.

[0105] Note should be taken that, in contrast thereto, exceeding the threshold 530, cf. FIG. 5a, once already suffices to detect an unusual event. Combinations of the two events are also conceivable.

[0106] FIG. 6 schematically shows four graphs 610, 620, 630 and 640 by way of example, said graphs elucidating the identification of unusual oscillation events according to the disclosure. The wind speed is plotted along the horizontal axis in all four graphs 610, 620, 630, 640 and said wind speed is plotted against a transverse direction tower head acceleration in graph 610, a longitudinal direction tower head acceleration in graph 630, a lateral direction mid tower oscillation in graph 620 and a longitudinal direction mid tower oscillation in graph 640.

[0107] A 5 t mass imbalance at a rotor radius of 1 m was simulated and the effect on the observed oscillation was evaluated. Three different formulations of a parametrized limit 602, 604 and 606 were provided for the respective graphs. The parametrizations are based on different concepts, for example different confidence intervals or different functions. Limit 602 has been chosen more aggressively, that is to say it is only exceeded at significantly higher accelerations, whereas the further limits 604 and 606 have been formulated more conservatively so that even relatively small oscillation events that exceed the acceleration limit are rendered detectable.

[0108] Naturally, the parametrized limits 602, 604, 606 are dependent on the observed oscillation and are only labelled by the same reference symbol for the purposes of simplifying the illustration. On account of the different observed oscillations, the values of the limits 602, 604, 606 will also be different in the graphs 610, 620, 630, 640, as illustrated.

[0109] It is evident that significant overshoots of the limits 602, 604, 606 are evident only in graph 610 for the transverse direction tower head acceleration, said overshoots being grouped in a region 612. The unexpected high accelerations at low wind speeds can be traced back, as theoretically expected, to stimulation of a first tower eigenmode in the lateral direction, that is to say to a tower head oscillation.

[0110] A minor overshoot of the limits can also be detected for the X- and Y-accelerations mid tower and for the X-acceleration or longitudinal acceleration at the tower head. The corresponding points are labelled by 622, 632 and 642. By way of example, such minor overshoots can be classified as extraordinary cases.

[0111] It is evident that, for example for the case of a rotor imbalance, the transverse direction tower head acceleration in graph 610 exhibits the clearest excursions. However, minor effects can also be detected in other observed oscillations.

[0112] The method according to the disclosure therefore consists of the identification of dependencies and correlations between accelerations or speeds and ambient parameters, that is to say states of the wind power installation or the surroundings. Limit values are defined for the accelerations or oscillations of the produced electrical energy, said accelerations or oscillations depending on relevant ambient states such as the wind speed, for example. The sensitivity of the limit values in respect of various disturbances is analyzed and the defined limit values are therefore checked. Optionally, the defined limit values are adjusted, for example whenever a limit is exceeded too frequently.

[0113] Moreover, the possibility of a continuous improvement is provided since the monitoring system, once implemented, can be operated permanently on all wind power installations equipped therewith. This facilitates a permanent improvement in the determined limits, as a result of which the identification of unusual oscillation events is made permanently more precise.

[0114] Therefore, the disclosure can be divided into the following constituent parts: Initially, in a first step, suitable limits are determined and fitting dependencies are identified, for example offline. These are then compared online or in real time with current values. Finally, in a third step, there is an automatic adjustment of the previously determined limits on the basis of the actual load of the respective wind power installation, in particular also online or in real time.

[0115] For the first function, an unusual oscillation event is essentially defined by the installation operator or installation manufacturer as an oscillation that deviates from the simulation. By way of example, the oscillation that deviates from the simulation may have a stronger amplitude.

[0116] Limits are determined for the observed oscillation on the basis of simulations, for example 10 minute mean wind speeds in for example 2 m/s steps in a wind speed range from 2 to 28 m/s, the limit values naturally only being exemplary. Additionally, for example as explained above, shorter reference time periods for the wind speed, in particular of less than 1 minute, are advantageous.

[0117] Particularly preferably, the simulations model the operating load level of the wind power installation 100 during normal operation DLC 1.2 pursuant to DIN EN IEC 61400-1.

[0118] To determine the limits, use is made of the same operating values as in the installation controller itself. The maximum oscillations, for example measurements of tower oscillations, and the simultaneous wind speed, for example the wind speed obtained by a wind estimator, are read to determine the limits.

[0119] In this case, the wind speed is used as an example of an ambient parameter, with an alternative or additional use of shear and/or turbulence or further suitable ambient parameters being possible in the same way.

[0120] These are then grouped on the basis of the ambient parameter, for example the wind speed, with an increment of for example 1 m/s—once again for the wind speed—lending itself to this end. Then, a limit is determined for each of the groups of the values of the ambient parameter, for example with the aid of a quantile determination. This limit is then provided as a parametrized limit, which in this case depends on the ambient parameter such as the wind speed, for example.

[0121] In the second constituent part, the comparison of current values to limits, the obtained measurement data are filtered in particular. The operating state of the wind power installation 100 is then preferably determined, a mean, for example a moving 10 second mean, of the ambient parameter lending itself to this end. A current value and preferably also a moving mean are determined for the oscillation to be observed. In particular, the peak value of the oscillation is taken as the moving mean.

[0122] Then, the current limit is determined on the basis of the parametrized limit determined in the first constituent part, with interpolation between the points, in particular a linear interpolation, being implemented in an embodiment should there be a deviation of the current value of the ambient parameter from the granulation of the parametrized limit. This is followed by the comparison of the values to be monitored or observed with the limits.

[0123] Should a limit overshoot be detected, an information notification with the high-resolution operating measurement data can be transmitted in a preferred configuration, for example to SCADA, as a result of which it is available for analysis purposes, for example by a control engineer. The resolution of the operating measurement data is alterable and may be set to 10 Hz, for example.

[0124] A limit overshoot is an indication that the wind power installation 100 is experiencing an operating situation with increased load. The utilization moreover depends on location-specific conditions, for example. To take care of these factors that are specific to the wind power installation 100, a further adjustment of the parametrized limits in accordance with the situation is implemented. If the limit was exceeded, the limit is increased by a factor in particular; if the limit has not been exceeded for a certain period of time, the limit is accordingly reduced by a factor.

[0125] It may transpire that the present disclosure is advantageous in the real time control of a single wind power installation 100 on the basis of the measured oscillations.

[0126] The present disclosure facilitates the identification, in particular the automatic identification, of oscillation events such as tower oscillation events, which in the case of a permanent occurrence may lead to the considered material fatigue loads being exceeded. This should be identified in a timely fashion such that the option is provided to react to this and, for example, make adjustments to the control. Measures may be rectified, but are not necessarily rectified, by software; events identified in this way may also be rectified by hardware measures, for example. Preference is given for the mitigation measure to always be defined by the cause identified from this event.

[0127] Moreover, the present disclosure allows the location-specific load utilization to be estimated by using the automatically settling limit multiplier. Preferably, the limit multiplier can be extended by the introduction of the wind speed dependence, in order also to take account of a wind distribution when estimating a load utilization. Naturally, the present disclosure is also applicable to other oscillation or load measurements, for example the blade load measurement, and not restricted to the tower oscillations described in exemplary fashion.

[0128] Accordingly, the present disclosure allows the identification of an unusual oscillation event in correlation with the simultaneous operating state of the wind power installation 100, the operating state being defined in particular by wind speed, power, rotational speed and pitch angle.

[0129] The solution according to the disclosure can be implemented in a control software of the individual wind power installation 100. As a result, no additional material and/or commissioning costs arise. Preferably, the communication of unusual oscillation events with high-resolution operating measurement data is implemented automatically and in real time. Preferably, the wind power installation 100 monitoring is active at all times, for as long as the wind power installation 100 is in operation. By virtue of taking the wind speed estimated by a wind estimator as the wind speed, use is made of a characteristic in the algorithm which determines the operating state of the wind power installation 100 very well and in one dimension.

[0130] By way of example, extensions to the present disclosure include a shear and turbulence dependence. As a result, the parametrized limit can be determined even more accurately and the classification of oscillation events as usual or unusual oscillation events is refined further.

[0131] In a further configuration, summary information is generated, for example on a monthly basis. This can provide the installation operator with an overview of the state of their wind power installation 100 at regular intervals. By way of example, the report documents all unusual oscillation events detected during the report period.

[0132] Finally, in the development of the algorithm, an identification of the excitation of the observed unusual oscillation event is implemented. By way of example, this allows identification of a 1P excitation, from which it is possible to deduce that the rotor or the rotor rotation is the cause for this oscillation excitation, or the identification of a 3P excitation, which can be connected to the rotor blade transition.

[0133] The identification may contain an automatic classification of the event, for example into “already known oscillation event” or “previously unknown oscillation event.” Other classifications such as “usual” or “unusual” are also conceivable.

[0134] The oscillation monitoring may or may not prompt further steps, depending on the identification or classification of the event. By way of example, a communication or notification may be abstained from if the event is assigned a certain classification, for example if a comparable event was previously already labelled as “conventional” or “known.” In other configurations, the execution of further steps may also be coupled positively to a certain classification, and so the steps are only carried out if a certain classification result is present.

[0135] Now, further applications of the oscillation monitoring and of the disclosed method to observed oscillations are described.

[0136] In one embodiment, blade oscillations of the rotor blades in the rotor plane are observed. Edgewise blade oscillations or blade swivel loads are decisively dominated by gravitation, and hence by the 1P frequency component. This simplifies the recognition of additional unusual oscillations. The gravitation-driven 1P frequency component can either be high-pass filtered or removed by calculation using an estimate. To estimate the gravitation component, use is made, in particular, of a rotor position, a blade mass, gravitation and a center of mass of the rotor.

[0137] By way of example, if structure-dynamic vibrations are overlaid on the blade oscillations or blade swivel loads, these can be identified as a result. By monitoring the “residual load,” which essentially comprises structure resonant frequencies, it is possible to automatically identify the dominant type of oscillation.

[0138] This facilitates automated monitoring of resonant oscillations which cannot be identified directly, for example by tower oscillation monitoring. This is the case since the blade swivel load is not pronounced in the tower head oscillation, for example. An example of such an oscillation is a collective rotor swivel mode.

[0139] Pitch oscillations can be evaluated in the same way. In the case where an individual blade adjustment of the individual rotor blades is possible, a mean pitch angle, in particular, is suitable for the observation of an oscillation of the pitch angle. By way of example, pitch oscillations can be excited or impressed by a rotational speed control resonance. To eliminate wind-excited oscillations, there can preferably be high-pass filtering with a cut-off frequency of approximately 0.05 Hz, for example.

[0140] The wind speed dependence is given by the blade sensitivity, which increases with increasing pitch angle. Singular and prolonged oscillation events can be recognized, with singular oscillation events for example being able to be traced back to, or meaning, control interventions or interventions of a controller for avoiding extreme loads, and prolonged oscillation events representing for example an input coupling of a rotational speed oscillation in the rotational speed control, for example a transverse tower oscillation. Use is made of the same logic as in the case of tower oscillation monitoring.

[0141] To automatically identify a dominant type of oscillation of an identified unusual or unexpected oscillation, it is possible, for example, to carry out a frequency evaluation. The oscillation, for example the tower oscillation but any other observed oscillation, too, may have a dominant frequency depending on the structure dynamics of the wind power installation 100 and the rotational speed. One example for identifying this dominant frequency lies in a decomposition of the signal in Fourier space, for example by applying a Fourier transform to the signal, followed by grouping of the frequency ranges.

[0142] In this case, structure-dynamic and 1P frequencies, in particular, are taken into account. Subsequently, the grouped frequency spectrum is decomposed into dominant constituents, for example by way of amplitude comparisons with a mean amplitude. Whether or not one or more constituents of the decomposed spectrum are dominant is checked, for example, by comparison of the various components. In one example, a component of a frequency range may be dominant if it comprises more than a specified threshold of the entire spectrum. By way of example, a constituent may be referred to as dominant if it comprises more than 75% of the spectrum in relation to the amplitude. Naturally, this value of 75% is merely exemplary and may also be adjusted depending on the observed oscillation.

[0143] Should one or more constituents be determined as dominant, a check is finally carried out as to whether this frequency could be considered causal. In this context, the goal is to exclude, e.g., 1P excitations, which can be traced back to gravitation for example. Accordingly, the last step of the check is a plausibility test.

[0144] Even if the evaluation of the frequencies is described by way of a Fourier analysis of the signal, all further known methods for frequency analysis of the signal are naturally likewise applicable.

[0145] In a further aspect, it is possible to learn additional dependencies which lead to an increase or reduction in the parametrized limit or expected level of the observed oscillation. This includes a shear, a turbulence intensity, the wind direction, in particular on account of follow-on effects of other wind power installations, obstacles, etc. Particularly preferably, the turbulence has a dependence on the wind direction in such cases. The adjustment factor of the parametrized limit may be extended depending on for example these quantities to form a utilization factor which can be coupled to an estimate of the load utilization, in particular with an appropriate dependence on and knowledge of the wind history. In particular, the wind history comprises an occurrence frequency of wind speeds, turbulence intensities, shear, etc.

[0146] Finally, FIGS. 7a and 7b schematically show the curve of an electrical power produced by the wind power installation 100 on the vertical axis against the wind speed on the horizontal axis by way of example. In particular, the power has been high-pass filtered in order to remove the wind influence. The four graphs 1010, 1020, 1030 and 1040 differ in terms of the length of the moving average window for averaging the power. The length is 60 seconds in graph 1010, 5 seconds in graph 1020, 2.5 seconds in graph 1030 and 10 seconds in graph 1040. A maximum 1002, a mean 1004 and a minimum 1006 of the power is depicted in each case. It is evident that as the length of the moving average is increasingly reduced, a better correlation of the power variation to the wind speed is obtained. This can be seen in graph 1030 in particular.

[0147] Accordingly, singular power drops, for example on account of an insufficient power adjustment at the generator, and prolonged under- and/or over-fulfilment of the power demand, for example on account of a wrong estimate of power losses, can be identified. Accordingly, it is possible to precisely monitor the observance of a specified characteristic, for example a rotational speed-power characteristic. As a result of adjusting the transformer power, prolonged deviations should not occur in this context during normal operation.

[0148] The various embodiments described above can be combined to provide further embodiments. These and other changes can be made to the embodiments in light of the above-detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure.